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qwen3.8-flash-next-unsloth-ud-q4_k_xl
Qwen3.8-Flash-Next, a 125B-parameter hybrid mixture-of-experts model, in
Unsloth's Dynamic quantization (UD-Q4_K_XL, 4-bit) (about 112 GB to download).
It reads images through the vision projector published for the original model,
and the Strata backend parses its reasoning and tool calls.
Needs about 88 GB of RAM to keep every expert in memory; with less, the mapped
experts are read from the SSD while it answers, which works and is much
slower. Keep the files on an SSD. Needs a GPU the strata backend supports;
NVIDIA with 12 GB of VRAM or more is the class Strata tests. Runs only on the
experimental strata backend.
On the first load the strata backend builds the Strata pack from the GGUF
files (a few minutes, a few GB of extra disk); a finished pack is never
rebuilt. The experts are read from the GGUF files in place (mapped); add
--resident-experts to engine_args to copy them into RAM when there is room.
Licensed under the Qwen Community License 1.0 (inherited from
Qwen/Qwen3.8-Flash-Next): free use, but a Model as a Service or AI Work
Assistant business needs a licence from Qwen. Read the LICENSE file in the
repository first.
The GGUF files are the Unsloth quantization; the vision projector is
ISTA-DASLab's file for the original model; Strata is by Niko1221. The MTP
draft layer is not configured here.
Repository: localaiLicense: qwen-community-1.0
qwen3.6-35b-a3b-ud-q4_k_xl-strata
Qwen3.6-35B-A3B, a 35B-parameter mixture-of-experts model with 3B active
parameters, in Unsloth's 4-bit (Unsloth Dynamic), the smallest GGUF with the
MTP layer (about 24 GB to download). It reads images through Unsloth's vision
projector, and the Strata backend parses its reasoning and tool calls.
Needs an NVIDIA or AMD GPU (the Intel Arc and Apple builds of Strata do not
run this model) and about 32 GB of RAM: the engine keeps every expert it can
in VRAM and computes the rest on the CPU. Runs only on the experimental strata
backend.
On the first load the strata backend exports the tokenizer and chat template
from the GGUF file (seconds); the engine reads the GGUF as it is. The MTP
draft layer inside the file is used for speculative decoding.
Licensed under the Apache License 2.0 (Qwen/Qwen3.6-35B-A3B). The GGUF files
are the Unsloth quantization; Strata is by Niko1221.
Repository: localaiLicense: apache-2.0
q3.5-bluestar-27b
Repository: localaiLicense: mit
liquidai.lfm2-2.6b-transcript
This is a large language model (2.6B parameters) designed for text-generation tasks. It is a quantized version of the original model `LiquidAI/LFM2-2.6B-Transcript`, optimized for efficiency while retaining strong performance. The model is built on the foundation of the base model, with additional optimizations for deployment and use cases like transcription or language modeling. It is trained on large-scale text data and supports multiple languages.
Repository: localai
depth-anything-3-base
Depth Anything 3 (base) monocular metric depth + camera pose, served via the
native depth-anything.cpp backend (C++/ggml + purego, no Python at inference).
Given an image it returns a dense depth map plus the recovered camera
extrinsics (3x4) and intrinsics (3x3). Use GenerateImage (src -> normalized
depth PNG at dst) or Predict (JSON depth stats + pose). q4_k is the
recommended CPU default.
Repository: localaiLicense: apache-2.0
depth-anything-3-base-q8_0
Depth Anything 3 (base), q8_0 — near-lossless 8-bit quant (~149 MB). Same
depth + camera pose output as the q4_k default at higher fidelity.
Repository: localaiLicense: apache-2.0
depth-anything-2-base
Depth Anything V2 (base / ViT-B) monocular depth, served via the native
depth-anything.cpp backend (C++/ggml + purego, no Python at inference).
Given an image it returns a dense monocular depth map only — no camera pose,
no confidence. This is the relative variant (relative inverse depth). Use
GenerateImage (src -> normalized depth PNG at dst) or the Depth endpoint.
q4_k is the recommended CPU default.
Repository: localaiLicense: apache-2.0
depth-anything-2-base-q8_0
Depth Anything V2 (base / ViT-B), q8_0 — near-lossless 8-bit quant. Same
relative monocular depth output as the q4_k default at higher fidelity. Use
GenerateImage (src -> depth PNG) or the Depth endpoint.
Repository: localaiLicense: apache-2.0
depth-anything-2-metric-hypersim-small
Depth Anything V2 Metric (Hypersim, indoor / ViT-S), q4_k — metric monocular
depth in METRES (indoor, max_depth 20). Depth only (no pose). Use
GenerateImage (src -> depth PNG) or the Depth endpoint.
Repository: localaiLicense: apache-2.0
depth-anything-2-metric-hypersim-base
Depth Anything V2 Metric (Hypersim, indoor / ViT-B), q4_k — metric monocular
depth in METRES (indoor, max_depth 20). Depth only (no pose). Use
GenerateImage (src -> depth PNG) or the Depth endpoint.
Repository: localaiLicense: apache-2.0
depth-anything-2-metric-hypersim-large
Depth Anything V2 Metric (Hypersim, indoor / ViT-L), q4_k — highest-quality
metric monocular depth in METRES (indoor, max_depth 20). Depth only (no
pose). Use GenerateImage (src -> depth PNG) or the Depth endpoint.
Repository: localaiLicense: apache-2.0
depth-anything-2-metric-vkitti-small
Depth Anything V2 Metric (Virtual KITTI, outdoor / ViT-S), q4_k — metric
monocular depth in METRES (outdoor, max_depth 80). Depth only (no pose). Use
GenerateImage (src -> depth PNG) or the Depth endpoint.
Repository: localaiLicense: apache-2.0
depth-anything-2-metric-vkitti-base
Depth Anything V2 Metric (Virtual KITTI, outdoor / ViT-B), q4_k — metric
monocular depth in METRES (outdoor, max_depth 80). Depth only (no pose). Use
GenerateImage (src -> depth PNG) or the Depth endpoint.
Repository: localaiLicense: apache-2.0
depth-anything-2-metric-vkitti-large
Depth Anything V2 Metric (Virtual KITTI, outdoor / ViT-L), q4_k —
highest-quality metric monocular depth in METRES (outdoor, max_depth 80).
Depth only (no pose). Use GenerateImage (src -> depth PNG) or the Depth
endpoint.
Repository: localaiLicense: apache-2.0
llama-3.2-1b-instruct:q4_k_m
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
Model Developer: Meta
Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
Repository: localaiLicense: llama3.2
llama-3.2-3b-instruct:q4_k_m
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
Model Developer: Meta
Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
Repository: localaiLicense: llama3.2
llama-3.1-storm-8b-q4_k_m
We present the Llama-3.1-Storm-8B model that outperforms Meta AI's Llama-3.1-8B-Instruct and Hermes-3-Llama-3.1-8B models significantly across diverse benchmarks as shown in the performance comparison plot in the next section. Our approach consists of three key steps:
- Self-Curation: We applied two self-curation methods to select approximately 1 million high-quality examples from a pool of about 3 million open-source examples. Our curation criteria focused on educational value and difficulty level, using the same SLM for annotation instead of larger models (e.g. 70B, 405B).
- Targeted fine-tuning: We performed Spectrum-based targeted fine-tuning over the Llama-3.1-8B-Instruct model. The Spectrum method accelerates training by selectively targeting layer modules based on their signal-to-noise ratio (SNR), and freezing the remaining modules. In our work, 50% of layers are frozen.
- Model Merging: We merged our fine-tuned model with the Llama-Spark model using SLERP method. The merging method produces a blended model with characteristics smoothly interpolated from both parent models, ensuring the resultant model captures the essence of both its parents. Llama-3.1-Storm-8B improves Llama-3.1-8B-Instruct across 10 diverse benchmarks. These benchmarks cover areas such as instruction-following, knowledge-driven QA, reasoning, truthful answer generation, and function calling.
Repository: localaiLicense: llama3.1
tulu-3.1-8b-supernova-smart
This model was merged using the passthrough merge method using bunnycore/Tulu-3.1-8B-SuperNova + bunnycore/Llama-3.1-8b-smart-lora as a base.
Repository: localaiLicense: llama3.1
wan-2.1-i2v-14b-720p-ggml
Wan 2.1 I2V 14B 720P — image-to-video diffusion, GGUF Q4_K_M.
Native 720p sibling of the 480p I2V model: animates a single
reference image into a 33-frame clip at up to 1280x720. Trained
purely as image-to-video (no first-last-frame interpolation path),
so motion is freer and better-suited to single-anchor animation
than repurposing the FLF2V 720P variant for i2v. Shares the same
VAE, umt5_xxl text encoder, and clip_vision_h as the I2V 14B 480P
and FLF2V 14B 720P entries.
Repository: localaiLicense: apache-2.0
kimodo-soma-rp-q4_k_m
Kimodo SOMA RP v1.1 text-to-motion on CPU or Vulkan, with F32 motion weights
and the shared monolithic Q4_K_M Llama-3/LLM2Vec text encoder.
Exports an animated skeleton GLB without a mesh or skin. Motion and text
weights retain their respective NVIDIA Open Model License and Llama 3 terms.
Repository: localai
kimodo-soma-rp-q4_k
Kimodo SOMA RP v1.1 text-to-motion on CPU or Vulkan, with F32 motion weights
and the shared monolithic Q4_K Llama-3/LLM2Vec text encoder.
Exports an animated skeleton GLB without a mesh or skin. Motion and text
weights retain their respective NVIDIA Open Model License and Llama 3 terms.
Repository: localai
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